{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 滤波"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 滤波代码\n",
    "import pywt\n",
    "from scipy import signal\n",
    "# import matplotlib.pyplot as plt\n",
    "\n",
    "def Wavelet_transform(ECG_Q):\n",
    "    w = pywt.Wavelet('db8') # 选用Daubechies8小波\n",
    "    maxlev = pywt.dwt_max_level(len(ECG_Q), w.dec_len)\n",
    "    threshold = 0.1 # Threshold for filtering\n",
    "    coeffs = pywt.wavedec(ECG_Q,'db8', level=maxlev) # 将信号进行小波分解\n",
    "    #这里就是对每一层的coffe进行更改\n",
    "    for i in range(1, len(coeffs)):\n",
    "        coeffs[i] = pywt.threshold(coeffs[i], threshold*max(coeffs[i])) \n",
    "    datarec = pywt.waverec(coeffs, 'db8') # 将信号进行小波重构\n",
    "    return datarec\n",
    "\n",
    "def butter_fliter(data, frequency, highpass, lowpass):\n",
    "    [b, a] = signal.butter(3, [lowpass / frequency * 2, highpass / frequency * 2], 'bandpass')\n",
    "    Signal_pro = signal.filtfilt(b, a, data)\n",
    "    return Signal_pro"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 读硬件给过来的信号"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import pickle \n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "\n",
    "# 模型的加载和使用\n",
    "with open(\"II. Classification(SVC)2.pickle\",\"rb\") as f:\n",
    "    SVM_load = pickle.load(f)\n",
    "\n",
    "test_ECG = pd.read_csv(\"../Datasets/1111ECG20230409165336.csv\",header=None).to_numpy().reshape([8192,])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8192,)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_ECG.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-05-05 15:01:46,546 - root - INFO - --------------------------------------------------\n",
      "2023-05-05 15:01:46,548 - root - INFO - 载入ECG的信号, 长度 = 8192 \n",
      "2023-05-05 15:01:46,549 - root - INFO - 调用 christov_segmenter 进行R波检测 ...\n",
      "2023-05-05 15:01:46,728 - root - INFO - 完成 christov_segmenter 用时: 0.178025 秒. \n",
      "2023-05-05 15:01:46,729 - root - INFO - 调用 hamilton_segmenter 进行R波检测 ...\n",
      "2023-05-05 15:01:46,749 - root - INFO - 完成 hamilton_segmenter 用时: 0.018390 秒. \n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "import logging\n",
    "import numpy as np\n",
    "from biosppy.signals import ecg\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 日志打印格式设定\n",
    "logging.basicConfig(level = logging.DEBUG,format = '%(asctime)s - %(name)s - %(levelname)s - %(message)s',force=True)\n",
    "logger = logging.getLogger(__name__)\n",
    "\n",
    "# record = 'ECG_Raw_0401'\n",
    "\n",
    "logging.info(\"--------------------------------------------------\")\n",
    "logging.info(\"载入ECG的信号, 长度 = %d \" % len(test_ECG))\n",
    "fs = 512  # 采样率为512 Hz\n",
    "logging.info(\"调用 christov_segmenter 进行R波检测 ...\")\n",
    "tic = time.time()\n",
    "rpeaks = ecg.christov_segmenter(test_ECG, sampling_rate=fs) # 调用christov_segmenter\n",
    "toc = time.time()\n",
    "logging.info(\"完成 christov_segmenter 用时: %f 秒. \" % (toc - tic))\n",
    "rpeaks_indices_1 = rpeaks[0]\n",
    "logging.info(\"调用 hamilton_segmenter 进行R波检测 ...\")\n",
    "tic = time.time()\n",
    "rpeaks = ecg.hamilton_segmenter(test_ECG, sampling_rate=fs) # 调用christov_segmenter\n",
    "toc = time.time()\n",
    "logging.info(\"完成 hamilton_segmenter 用时: %f 秒. \" % (toc - tic))\n",
    "rpeaks_indices_2 = rpeaks[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "手动计算该段时间被检测者的平均心率是121.875\n"
     ]
    }
   ],
   "source": [
    "BPM = (rpeaks_indices_1.shape[0] + rpeaks_indices_2.shape[0])/2 /16 *60\n",
    "print(\"手动计算该段时间被检测者的平均心率是{}\".format(BPM))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-05-04 21:22:57,421 - root - INFO - 绘制波形图和检测的R波位置 ...\n",
      "2023-05-04 21:22:57,426 - matplotlib.pyplot - DEBUG - Loaded backend module://matplotlib_inline.backend_inline version unknown.\n",
      "2023-05-04 21:22:57,428 - matplotlib.pyplot - DEBUG - Loaded backend module://matplotlib_inline.backend_inline version unknown.\n",
      "2023-05-04 21:22:57,432 - matplotlib.font_manager - DEBUG - findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
      "2023-05-04 21:22:57,433 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Users\\\\Qian Lv\\\\AppData\\\\Roaming\\\\Python\\\\Python311\\\\site-packages\\\\matplotlib\\\\mpl-data\\\\fonts\\\\ttf\\\\STIXNonUniIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n",
      "2023-05-04 21:22:57,433 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Users\\\\Qian Lv\\\\AppData\\\\Roaming\\\\Python\\\\Python311\\\\site-packages\\\\matplotlib\\\\mpl-data\\\\fonts\\\\ttf\\\\DejaVuSerif-Italic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n",
      "2023-05-04 21:22:57,435 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Users\\\\Qian Lv\\\\AppData\\\\Roaming\\\\Python\\\\Python311\\\\site-packages\\\\matplotlib\\\\mpl-data\\\\fonts\\\\ttf\\\\cmss10.ttf', name='cmss10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,436 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Users\\\\Qian Lv\\\\AppData\\\\Roaming\\\\Python\\\\Python311\\\\site-packages\\\\matplotlib\\\\mpl-data\\\\fonts\\\\ttf\\\\STIXGeneralBolIta.ttf', name='STIXGeneral', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n",
      "2023-05-04 21:22:57,436 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Users\\\\Qian Lv\\\\AppData\\\\Roaming\\\\Python\\\\Python311\\\\site-packages\\\\matplotlib\\\\mpl-data\\\\fonts\\\\ttf\\\\DejaVuSans.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 0.05\n",
      "2023-05-04 21:22:57,437 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Users\\\\Qian Lv\\\\AppData\\\\Roaming\\\\Python\\\\Python311\\\\site-packages\\\\matplotlib\\\\mpl-data\\\\fonts\\\\ttf\\\\STIXSizTwoSymBol.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "2023-05-04 21:22:57,437 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Users\\\\Qian Lv\\\\AppData\\\\Roaming\\\\Python\\\\Python311\\\\site-packages\\\\matplotlib\\\\mpl-data\\\\fonts\\\\ttf\\\\DejaVuSansMono-Bold.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "2023-05-04 21:22:57,438 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Users\\\\Qian Lv\\\\AppData\\\\Roaming\\\\Python\\\\Python311\\\\site-packages\\\\matplotlib\\\\mpl-data\\\\fonts\\\\ttf\\\\STIXSizFourSymBol.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "2023-05-04 21:22:57,439 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Users\\\\Qian Lv\\\\AppData\\\\Roaming\\\\Python\\\\Python311\\\\site-packages\\\\matplotlib\\\\mpl-data\\\\fonts\\\\ttf\\\\STIXSizThreeSymReg.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,439 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Users\\\\Qian Lv\\\\AppData\\\\Roaming\\\\Python\\\\Python311\\\\site-packages\\\\matplotlib\\\\mpl-data\\\\fonts\\\\ttf\\\\DejaVuSerifDisplay.ttf', name='DejaVu Serif Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,440 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Users\\\\Qian Lv\\\\AppData\\\\Roaming\\\\Python\\\\Python311\\\\site-packages\\\\matplotlib\\\\mpl-data\\\\fonts\\\\ttf\\\\STIXNonUni.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,442 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Users\\\\Qian Lv\\\\AppData\\\\Roaming\\\\Python\\\\Python311\\\\site-packages\\\\matplotlib\\\\mpl-data\\\\fonts\\\\ttf\\\\STIXSizFiveSymReg.ttf', name='STIXSizeFiveSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,443 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Users\\\\Qian Lv\\\\AppData\\\\Roaming\\\\Python\\\\Python311\\\\site-packages\\\\matplotlib\\\\mpl-data\\\\fonts\\\\ttf\\\\DejaVuSerif.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,443 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Users\\\\Qian Lv\\\\AppData\\\\Roaming\\\\Python\\\\Python311\\\\site-packages\\\\matplotlib\\\\mpl-data\\\\fonts\\\\ttf\\\\STIXSizOneSymReg.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,444 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Users\\\\Qian Lv\\\\AppData\\\\Roaming\\\\Python\\\\Python311\\\\site-packages\\\\matplotlib\\\\mpl-data\\\\fonts\\\\ttf\\\\STIXSizThreeSymBol.ttf', name='STIXSizeThreeSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "2023-05-04 21:22:57,444 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Users\\\\Qian Lv\\\\AppData\\\\Roaming\\\\Python\\\\Python311\\\\site-packages\\\\matplotlib\\\\mpl-data\\\\fonts\\\\ttf\\\\DejaVuSerif-Bold.ttf', name='DejaVu Serif', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "2023-05-04 21:22:57,445 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Users\\\\Qian Lv\\\\AppData\\\\Roaming\\\\Python\\\\Python311\\\\site-packages\\\\matplotlib\\\\mpl-data\\\\fonts\\\\ttf\\\\cmb10.ttf', name='cmb10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,445 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Users\\\\Qian Lv\\\\AppData\\\\Roaming\\\\Python\\\\Python311\\\\site-packages\\\\matplotlib\\\\mpl-data\\\\fonts\\\\ttf\\\\STIXSizTwoSymReg.ttf', name='STIXSizeTwoSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,446 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Users\\\\Qian Lv\\\\AppData\\\\Roaming\\\\Python\\\\Python311\\\\site-packages\\\\matplotlib\\\\mpl-data\\\\fonts\\\\ttf\\\\DejaVuSansMono.ttf', name='DejaVu Sans Mono', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,449 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Users\\\\Qian Lv\\\\AppData\\\\Roaming\\\\Python\\\\Python311\\\\site-packages\\\\matplotlib\\\\mpl-data\\\\fonts\\\\ttf\\\\STIXGeneral.ttf', name='STIXGeneral', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,450 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Users\\\\Qian Lv\\\\AppData\\\\Roaming\\\\Python\\\\Python311\\\\site-packages\\\\matplotlib\\\\mpl-data\\\\fonts\\\\ttf\\\\STIXGeneralItalic.ttf', name='STIXGeneral', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n",
      "2023-05-04 21:22:57,450 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Users\\\\Qian Lv\\\\AppData\\\\Roaming\\\\Python\\\\Python311\\\\site-packages\\\\matplotlib\\\\mpl-data\\\\fonts\\\\ttf\\\\cmr10.ttf', name='cmr10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,450 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Users\\\\Qian Lv\\\\AppData\\\\Roaming\\\\Python\\\\Python311\\\\site-packages\\\\matplotlib\\\\mpl-data\\\\fonts\\\\ttf\\\\DejaVuSansDisplay.ttf', name='DejaVu Sans Display', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,452 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Users\\\\Qian Lv\\\\AppData\\\\Roaming\\\\Python\\\\Python311\\\\site-packages\\\\matplotlib\\\\mpl-data\\\\fonts\\\\ttf\\\\DejaVuSansMono-BoldOblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n",
      "2023-05-04 21:22:57,453 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Users\\\\Qian Lv\\\\AppData\\\\Roaming\\\\Python\\\\Python311\\\\site-packages\\\\matplotlib\\\\mpl-data\\\\fonts\\\\ttf\\\\DejaVuSansMono-Oblique.ttf', name='DejaVu Sans Mono', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n",
      "2023-05-04 21:22:57,453 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Users\\\\Qian Lv\\\\AppData\\\\Roaming\\\\Python\\\\Python311\\\\site-packages\\\\matplotlib\\\\mpl-data\\\\fonts\\\\ttf\\\\STIXNonUniBolIta.ttf', name='STIXNonUnicode', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n",
      "2023-05-04 21:22:57,454 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Users\\\\Qian Lv\\\\AppData\\\\Roaming\\\\Python\\\\Python311\\\\site-packages\\\\matplotlib\\\\mpl-data\\\\fonts\\\\ttf\\\\cmtt10.ttf', name='cmtt10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,455 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Users\\\\Qian Lv\\\\AppData\\\\Roaming\\\\Python\\\\Python311\\\\site-packages\\\\matplotlib\\\\mpl-data\\\\fonts\\\\ttf\\\\DejaVuSerif-BoldItalic.ttf', name='DejaVu Serif', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n",
      "2023-05-04 21:22:57,456 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Users\\\\Qian Lv\\\\AppData\\\\Roaming\\\\Python\\\\Python311\\\\site-packages\\\\matplotlib\\\\mpl-data\\\\fonts\\\\ttf\\\\DejaVuSans-Bold.ttf', name='DejaVu Sans', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 0.33499999999999996\n",
      "2023-05-04 21:22:57,456 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Users\\\\Qian Lv\\\\AppData\\\\Roaming\\\\Python\\\\Python311\\\\site-packages\\\\matplotlib\\\\mpl-data\\\\fonts\\\\ttf\\\\STIXSizOneSymBol.ttf', name='STIXSizeOneSym', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "2023-05-04 21:22:57,457 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Users\\\\Qian Lv\\\\AppData\\\\Roaming\\\\Python\\\\Python311\\\\site-packages\\\\matplotlib\\\\mpl-data\\\\fonts\\\\ttf\\\\DejaVuSans-Oblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 1.05\n",
      "2023-05-04 21:22:57,459 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Users\\\\Qian Lv\\\\AppData\\\\Roaming\\\\Python\\\\Python311\\\\site-packages\\\\matplotlib\\\\mpl-data\\\\fonts\\\\ttf\\\\cmsy10.ttf', name='cmsy10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,460 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Users\\\\Qian Lv\\\\AppData\\\\Roaming\\\\Python\\\\Python311\\\\site-packages\\\\matplotlib\\\\mpl-data\\\\fonts\\\\ttf\\\\cmmi10.ttf', name='cmmi10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,460 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Users\\\\Qian Lv\\\\AppData\\\\Roaming\\\\Python\\\\Python311\\\\site-packages\\\\matplotlib\\\\mpl-data\\\\fonts\\\\ttf\\\\DejaVuSans-BoldOblique.ttf', name='DejaVu Sans', style='oblique', variant='normal', weight=700, stretch='normal', size='scalable')) = 1.335\n",
      "2023-05-04 21:22:57,461 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Users\\\\Qian Lv\\\\AppData\\\\Roaming\\\\Python\\\\Python311\\\\site-packages\\\\matplotlib\\\\mpl-data\\\\fonts\\\\ttf\\\\STIXGeneralBol.ttf', name='STIXGeneral', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "2023-05-04 21:22:57,462 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Users\\\\Qian Lv\\\\AppData\\\\Roaming\\\\Python\\\\Python311\\\\site-packages\\\\matplotlib\\\\mpl-data\\\\fonts\\\\ttf\\\\cmex10.ttf', name='cmex10', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,462 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Users\\\\Qian Lv\\\\AppData\\\\Roaming\\\\Python\\\\Python311\\\\site-packages\\\\matplotlib\\\\mpl-data\\\\fonts\\\\ttf\\\\STIXSizFourSymReg.ttf', name='STIXSizeFourSym', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,463 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Users\\\\Qian Lv\\\\AppData\\\\Roaming\\\\Python\\\\Python311\\\\site-packages\\\\matplotlib\\\\mpl-data\\\\fonts\\\\ttf\\\\STIXNonUniBol.ttf', name='STIXNonUnicode', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "2023-05-04 21:22:57,464 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\pala.ttf', name='Palatino Linotype', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,465 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\HATTEN.TTF', name='Haettenschweiler', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,467 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\bahnschrift.ttf', name='Bahnschrift', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,467 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\SHOWG.TTF', name='Showcard Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,468 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\STXIHEI.TTF', name='STXihei', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,468 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\Nirmala.ttf', name='Nirmala UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,470 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\CENSCBK.TTF', name='Century Schoolbook', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,471 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\STXINGKA.TTF', name='STXingkai', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,472 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\BOD_CR.TTF', name='Bodoni MT', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25\n",
      "2023-05-04 21:22:57,473 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\BAUHS93.TTF', name='Bauhaus 93', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,473 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\MOD20.TTF', name='Modern No. 20', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,475 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\ANTQUAI.TTF', name='Book Antiqua', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n",
      "2023-05-04 21:22:57,475 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\FZSTK.TTF', name='FZShuTi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,476 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\Candara.ttf', name='Candara', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,476 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\YuGothL.ttc', name='Yu Gothic', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145\n",
      "2023-05-04 21:22:57,477 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\simhei.ttf', name='SimHei', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,478 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\tahoma.ttf', name='Tahoma', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,479 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\BOD_BLAR.TTF', name='Bodoni MT', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525\n",
      "2023-05-04 21:22:57,480 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\palabi.ttf', name='Palatino Linotype', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n",
      "2023-05-04 21:22:57,481 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\SitkaVF-Italic.ttf', name='Sitka', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n",
      "2023-05-04 21:22:57,482 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\GOTHIC.TTF', name='Century Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,483 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\BOOKOSBI.TTF', name='Bookman Old Style', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24\n",
      "2023-05-04 21:22:57,484 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\msyh.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,484 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\trebucbi.ttf', name='Trebuchet MS', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n",
      "2023-05-04 21:22:57,485 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\msjh.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,485 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\YuGothM.ttc', name='Yu Gothic', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n",
      "2023-05-04 21:22:57,486 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\OUTLOOK.TTF', name='MS Outlook', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,486 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\segoepr.ttf', name='Segoe Print', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,487 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\BOD_R.TTF', name='Bodoni MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,488 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\comici.ttf', name='Comic Sans MS', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n",
      "2023-05-04 21:22:57,488 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\STENCIL.TTF', name='Stencil', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,490 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\simsunb.ttf', name='SimSun-ExtB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,490 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\georgiaz.ttf', name='Georgia', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n",
      "2023-05-04 21:22:57,491 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\calibrii.ttf', name='Calibri', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n",
      "2023-05-04 21:22:57,492 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\taileb.ttf', name='Microsoft Tai Le', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "2023-05-04 21:22:57,492 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\FRABK.TTF', name='Franklin Gothic Book', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,493 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\segoeprb.ttf', name='Segoe Print', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "2023-05-04 21:22:57,494 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\LeelawUI.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,494 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\YuGothR.ttc', name='Yu Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,495 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\simsun.ttc', name='SimSun', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,496 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\LATINWD.TTF', name='Wide Latin', style='normal', variant='normal', weight=400, stretch='expanded', size='scalable')) = 10.25\n",
      "2023-05-04 21:22:57,496 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\GOUDOS.TTF', name='Goudy Old Style', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,497 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\LSANS.TTF', name='Lucida Sans', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,497 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\ROCKEB.TTF', name='Rockwell Extra Bold', style='normal', variant='normal', weight=800, stretch='normal', size='scalable')) = 10.43\n",
      "2023-05-04 21:22:57,499 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\couri.ttf', name='Courier New', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n",
      "2023-05-04 21:22:57,499 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\msyhl.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=290, stretch='normal', size='scalable')) = 10.1545\n",
      "2023-05-04 21:22:57,500 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\ANTQUABI.TTF', name='Book Antiqua', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n",
      "2023-05-04 21:22:57,500 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\GOTHICI.TTF', name='Century Gothic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n",
      "2023-05-04 21:22:57,501 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\COLONNA.TTF', name='Colonna MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,502 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\BOD_B.TTF', name='Bodoni MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "2023-05-04 21:22:57,502 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\GILI____.TTF', name='Gill Sans MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n",
      "2023-05-04 21:22:57,502 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\courbd.ttf', name='Courier New', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "2023-05-04 21:22:57,503 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\monbaiti.ttf', name='Mongolian Baiti', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,503 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\STKAITI.TTF', name='STKaiti', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,504 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\TCMI____.TTF', name='Tw Cen MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n",
      "2023-05-04 21:22:57,504 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\BASKVILL.TTF', name='Baskerville Old Face', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,505 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\FRADMIT.TTF', name='Franklin Gothic Demi', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n",
      "2023-05-04 21:22:57,506 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\corbel.ttf', name='Corbel', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,506 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\BROADW.TTF', name='Broadway', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,507 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\FRADMCN.TTF', name='Franklin Gothic Demi Cond', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25\n",
      "2023-05-04 21:22:57,507 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\segoeuil.ttf', name='Segoe UI', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145\n",
      "2023-05-04 21:22:57,509 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\CURLZ___.TTF', name='Curlz MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,509 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\BOD_CB.TTF', name='Bodoni MT', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535\n",
      "2023-05-04 21:22:57,510 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\ariblk.ttf', name='Arial', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 6.888636363636364\n",
      "2023-05-04 21:22:57,510 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\NirmalaS.ttf', name='Nirmala UI', style='normal', variant='normal', weight=350, stretch='normal', size='scalable')) = 10.0975\n",
      "2023-05-04 21:22:57,511 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\COPRGTB.TTF', name='Copperplate Gothic Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,512 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\HTOWERTI.TTF', name='High Tower Text', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n",
      "2023-05-04 21:22:57,512 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\SegUIVar.ttf', name='Segoe UI Variable', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,513 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\arialbd.ttf', name='Arial', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 6.698636363636363\n",
      "2023-05-04 21:22:57,514 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\LFAXD.TTF', name='Lucida Fax', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24\n",
      "2023-05-04 21:22:57,515 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\PERBI___.TTF', name='Perpetua', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n",
      "2023-05-04 21:22:57,515 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\BSSYM7.TTF', name='Bookshelf Symbol 7', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,516 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\LCALLIG.TTF', name='Lucida Calligraphy', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n",
      "2023-05-04 21:22:57,516 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\LTYPEBO.TTF', name='Lucida Sans Typewriter', style='oblique', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24\n",
      "2023-05-04 21:22:57,517 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\Candaral.ttf', name='Candara', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,517 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\msjhl.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=290, stretch='normal', size='scalable')) = 10.1545\n",
      "2023-05-04 21:22:57,517 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\GILLUBCD.TTF', name='Gill Sans Ultra Bold Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25\n",
      "2023-05-04 21:22:57,518 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\PLAYBILL.TTF', name='Playbill', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,518 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\LSANSDI.TTF', name='Lucida Sans', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24\n",
      "2023-05-04 21:22:57,519 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\mmrtextb.ttf', name='Myanmar Text', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "2023-05-04 21:22:57,519 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\arial.ttf', name='Arial', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 6.413636363636363\n",
      "2023-05-04 21:22:57,520 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\seguisym.ttf', name='Segoe UI Symbol', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,521 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\seguisbi.ttf', name='Segoe UI', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24\n",
      "2023-05-04 21:22:57,521 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\BRLNSB.TTF', name='Berlin Sans FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "2023-05-04 21:22:57,522 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\ARLRDBD.TTF', name='Arial Rounded MT Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,522 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\PERI____.TTF', name='Perpetua', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n",
      "2023-05-04 21:22:57,523 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\malgunsl.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145\n",
      "2023-05-04 21:22:57,524 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\javatext.ttf', name='Javanese Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,524 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\malgun.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,525 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\WINGDNG2.TTF', name='Wingdings 2', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,525 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\REFSAN.TTF', name='MS Reference Sans Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,525 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\calibrib.ttf', name='Calibri', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "2023-05-04 21:22:57,526 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\ARIALNI.TTF', name='Arial', style='italic', variant='normal', weight=400, stretch='condensed', size='scalable')) = 7.613636363636363\n",
      "2023-05-04 21:22:57,527 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\LFAXI.TTF', name='Lucida Fax', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n",
      "2023-05-04 21:22:57,527 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\WINGDNG3.TTF', name='Wingdings 3', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,528 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\symbol.ttf', name='Symbol', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,529 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\segmdl2.ttf', name='Segoe MDL2 Assets', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,530 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\Candaraz.ttf', name='Candara', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n",
      "2023-05-04 21:22:57,532 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\LSANSD.TTF', name='Lucida Sans', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24\n",
      "2023-05-04 21:22:57,533 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\GOUDYSTO.TTF', name='Goudy Stout', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,534 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\segoescb.ttf', name='Segoe Script', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "2023-05-04 21:22:57,534 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\LTYPEB.TTF', name='Lucida Sans Typewriter', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24\n",
      "2023-05-04 21:22:57,535 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\CALIFB.TTF', name='Californian FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "2023-05-04 21:22:57,535 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\STZHONGS.TTF', name='STZhongsong', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,536 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\CALISTBI.TTF', name='Calisto MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n",
      "2023-05-04 21:22:57,536 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\msgothic.ttc', name='MS Gothic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,537 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\GARA.TTF', name='Garamond', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,537 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\ebrimabd.ttf', name='Ebrima', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "2023-05-04 21:22:57,538 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\georgiai.ttf', name='Georgia', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n",
      "2023-05-04 21:22:57,538 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\BRLNSDB.TTF', name='Berlin Sans FB Demi', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "2023-05-04 21:22:57,539 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\ARIALNB.TTF', name='Arial', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 6.8986363636363635\n",
      "2023-05-04 21:22:57,539 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\seguibl.ttf', name='Segoe UI', style='normal', variant='normal', weight=900, stretch='normal', size='scalable')) = 10.525\n",
      "2023-05-04 21:22:57,539 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\GOUDOSI.TTF', name='Goudy Old Style', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n",
      "2023-05-04 21:22:57,541 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\trebucbd.ttf', name='Trebuchet MS', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "2023-05-04 21:22:57,542 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\gadugi.ttf', name='Gadugi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,543 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\DUBAI-LIGHT.TTF', name='Dubai', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145\n",
      "2023-05-04 21:22:57,543 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\consolab.ttf', name='Consolas', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "2023-05-04 21:22:57,544 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\ROCKI.TTF', name='Rockwell', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n",
      "2023-05-04 21:22:57,545 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\LBRITE.TTF', name='Lucida Bright', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,546 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\REFSPCL.TTF', name='MS Reference Specialty', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,547 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\calibriz.ttf', name='Calibri', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n",
      "2023-05-04 21:22:57,547 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\mvboli.ttf', name='MV Boli', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,548 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\STSONG.TTF', name='STSong', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,550 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\MATURASC.TTF', name='Matura MT Script Capitals', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,551 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\constanz.ttf', name='Constantia', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n",
      "2023-05-04 21:22:57,553 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\ERASBD.TTF', name='Eras Bold ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,554 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\gadugib.ttf', name='Gadugi', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "2023-05-04 21:22:57,555 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\segoeui.ttf', name='Segoe UI', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,557 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\TCCEB.TTF', name='Tw Cen MT Condensed Extra Bold', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25\n",
      "2023-05-04 21:22:57,559 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\Dengb.ttf', name='DengXian', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "2023-05-04 21:22:57,560 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\segoeuib.ttf', name='Segoe UI', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "2023-05-04 21:22:57,560 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\NIAGENG.TTF', name='Niagara Engraved', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,562 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\seguihis.ttf', name='Segoe UI Historic', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,564 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\PER_____.TTF', name='Perpetua', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,566 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\BERNHC.TTF', name='Bernard MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25\n",
      "2023-05-04 21:22:57,566 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\ERASMD.TTF', name='Eras Medium ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,567 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\SIMLI.TTF', name='LiSu', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,568 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\segoesc.ttf', name='Segoe Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,569 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\ONYX.TTF', name='Onyx', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,570 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\FRSCRIPT.TTF', name='French Script MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,571 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\STCAIYUN.TTF', name='STCaiyun', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,573 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\STFANGSO.TTF', name='STFangsong', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,575 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\LBRITEDI.TTF', name='Lucida Bright', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24\n",
      "2023-05-04 21:22:57,577 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\STXINWEI.TTF', name='STXinwei', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,578 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\ROCKB.TTF', name='Rockwell', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "2023-05-04 21:22:57,579 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\LeelaUIb.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "2023-05-04 21:22:57,579 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\NIAGSOL.TTF', name='Niagara Solid', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,580 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\corbelb.ttf', name='Corbel', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "2023-05-04 21:22:57,582 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\LTYPEO.TTF', name='Lucida Sans Typewriter', style='oblique', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n",
      "2023-05-04 21:22:57,582 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\mingliub.ttc', name='MingLiU-ExtB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,583 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\ITCEDSCR.TTF', name='Edwardian Script ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,583 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\ARIALNBI.TTF', name='Arial', style='italic', variant='normal', weight=700, stretch='condensed', size='scalable')) = 7.8986363636363635\n",
      "2023-05-04 21:22:57,584 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\calibril.ttf', name='Calibri', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145\n",
      "2023-05-04 21:22:57,584 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\sylfaen.ttf', name='Sylfaen', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,585 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\ariali.ttf', name='Arial', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 7.413636363636363\n",
      "2023-05-04 21:22:57,585 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\MTEXTRA.TTF', name='MT Extra', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,587 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\verdanai.ttf', name='Verdana', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 4.6863636363636365\n",
      "2023-05-04 21:22:57,587 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\palab.ttf', name='Palatino Linotype', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "2023-05-04 21:22:57,588 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\NirmalaB.ttf', name='Nirmala UI', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "2023-05-04 21:22:57,589 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\LBRITED.TTF', name='Lucida Bright', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24\n",
      "2023-05-04 21:22:57,589 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\BOD_CI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=400, stretch='condensed', size='scalable')) = 11.25\n",
      "2023-05-04 21:22:57,590 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\trebuc.ttf', name='Trebuchet MS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,591 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\GARABD.TTF', name='Garamond', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "2023-05-04 21:22:57,592 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\HARNGTON.TTF', name='Harrington', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,593 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\wingding.ttf', name='Wingdings', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,594 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\ERASDEMI.TTF', name='Eras Demi ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,595 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\verdanab.ttf', name='Verdana', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 3.9713636363636367\n",
      "2023-05-04 21:22:57,596 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\constanb.ttf', name='Constantia', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "2023-05-04 21:22:57,597 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\CHILLER.TTF', name='Chiller', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,598 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\tahomabd.ttf', name='Tahoma', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "2023-05-04 21:22:57,599 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\ebrima.ttf', name='Ebrima', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,600 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\FORTE.TTF', name='Forte', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,600 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\PERB____.TTF', name='Perpetua', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "2023-05-04 21:22:57,601 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\himalaya.ttf', name='Microsoft Himalaya', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,601 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\FELIXTI.TTF', name='Felix Titling', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,602 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\Candarai.ttf', name='Candara', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n",
      "2023-05-04 21:22:57,602 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\CENTAUR.TTF', name='Centaur', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,604 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\GILB____.TTF', name='Gill Sans MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "2023-05-04 21:22:57,604 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\georgia.ttf', name='Georgia', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,604 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\AGENCYB.TTF', name='Agency FB', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "2023-05-04 21:22:57,605 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\YuGothB.ttc', name='Yu Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "2023-05-04 21:22:57,606 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\BOD_I.TTF', name='Bodoni MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n",
      "2023-05-04 21:22:57,606 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\STLITI.TTF', name='STLiti', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,607 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\BOOKOSB.TTF', name='Bookman Old Style', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24\n",
      "2023-05-04 21:22:57,607 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\SitkaVF.ttf', name='Sitka', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,608 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\STHUPO.TTF', name='STHupo', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,609 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\ntailub.ttf', name='Microsoft New Tai Lue', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "2023-05-04 21:22:57,609 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\simkai.ttf', name='KaiTi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,610 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\palai.ttf', name='Palatino Linotype', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n",
      "2023-05-04 21:22:57,611 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\verdana.ttf', name='Verdana', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 3.6863636363636365\n",
      "2023-05-04 21:22:57,612 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\DUBAI-REGULAR.TTF', name='Dubai', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,612 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\CALIFI.TTF', name='Californian FB', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n",
      "2023-05-04 21:22:57,613 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\RAGE.TTF', name='Rage Italic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n",
      "2023-05-04 21:22:57,615 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\FRABKIT.TTF', name='Franklin Gothic Book', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n",
      "2023-05-04 21:22:57,616 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\phagspa.ttf', name='Microsoft PhagsPa', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,616 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\segoeuiz.ttf', name='Segoe UI', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n",
      "2023-05-04 21:22:57,617 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\ntailu.ttf', name='Microsoft New Tai Lue', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,617 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\BOD_BI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n",
      "2023-05-04 21:22:57,618 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\georgiab.ttf', name='Georgia', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "2023-05-04 21:22:57,619 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\l_10646.ttf', name='Lucida Sans Unicode', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,619 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\GILSANUB.TTF', name='Gill Sans Ultra Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,620 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\timesi.ttf', name='Times New Roman', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n",
      "2023-05-04 21:22:57,620 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\POORICH.TTF', name='Poor Richard', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,621 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\BELLB.TTF', name='Bell MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "2023-05-04 21:22:57,622 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\cambriab.ttf', name='Cambria', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "2023-05-04 21:22:57,623 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\FRAHVIT.TTF', name='Franklin Gothic Heavy', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n",
      "2023-05-04 21:22:57,624 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\comicz.ttf', name='Comic Sans MS', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n",
      "2023-05-04 21:22:57,624 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\PERTIBD.TTF', name='Perpetua Titling MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "2023-05-04 21:22:57,625 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\INFROMAN.TTF', name='Informal Roman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,626 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\seguisli.ttf', name='Segoe UI', style='italic', variant='normal', weight=350, stretch='normal', size='scalable')) = 11.0975\n",
      "2023-05-04 21:22:57,627 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\SegoeIcons.ttf', name='Segoe Fluent Icons', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,627 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\ITCKRIST.TTF', name='Kristen ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,627 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\ANTQUAB.TTF', name='Book Antiqua', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "2023-05-04 21:22:57,628 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\Gabriola.ttf', name='Gabriola', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,629 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\msjhbd.ttc', name='Microsoft JhengHei', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "2023-05-04 21:22:57,630 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\cambriai.ttf', name='Cambria', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n",
      "2023-05-04 21:22:57,632 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\arialbi.ttf', name='Arial', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 7.698636363636363\n",
      "2023-05-04 21:22:57,633 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\BELL.TTF', name='Bell MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,634 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\GOTHICBI.TTF', name='Century Gothic', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n",
      "2023-05-04 21:22:57,634 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\LeelUIsl.ttf', name='Leelawadee UI', style='normal', variant='normal', weight=350, stretch='normal', size='scalable')) = 10.0975\n",
      "2023-05-04 21:22:57,634 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\GOUDOSB.TTF', name='Goudy Old Style', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "2023-05-04 21:22:57,635 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\ITCBLKAD.TTF', name='Blackadder ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,636 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\cambria.ttc', name='Cambria', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,637 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\ELEPHNTI.TTF', name='Elephant', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n",
      "2023-05-04 21:22:57,637 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\comic.ttf', name='Comic Sans MS', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,638 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\simfang.ttf', name='FangSong', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,640 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\timesbi.ttf', name='Times New Roman', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n",
      "2023-05-04 21:22:57,641 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\consolaz.ttf', name='Consolas', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n",
      "2023-05-04 21:22:57,642 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\segoeuii.ttf', name='Segoe UI', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n",
      "2023-05-04 21:22:57,642 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\LFAX.TTF', name='Lucida Fax', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,643 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\GOTHICB.TTF', name='Century Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "2023-05-04 21:22:57,649 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\comicbd.ttf', name='Comic Sans MS', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "2023-05-04 21:22:57,651 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\FRADM.TTF', name='Franklin Gothic Demi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,653 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\TCCM____.TTF', name='Tw Cen MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25\n",
      "2023-05-04 21:22:57,655 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\SCHLBKI.TTF', name='Century Schoolbook', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n",
      "2023-05-04 21:22:57,655 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\PERTILI.TTF', name='Perpetua Titling MT', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145\n",
      "2023-05-04 21:22:57,658 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\OCRAEXT.TTF', name='OCR A Extended', style='normal', variant='normal', weight=400, stretch='expanded', size='scalable')) = 10.25\n",
      "2023-05-04 21:22:57,659 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\mmrtext.ttf', name='Myanmar Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,661 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\Candarali.ttf', name='Candara', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n",
      "2023-05-04 21:22:57,664 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\FZYTK.TTF', name='FZYaoTi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,665 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\MAGNETOB.TTF', name='Magneto', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "2023-05-04 21:22:57,668 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\Inkfree.ttf', name='Ink Free', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,670 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\CALIST.TTF', name='Calisto MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,672 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\BRITANIC.TTF', name='Britannic Bold', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,673 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\impact.ttf', name='Impact', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,674 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\corbelli.ttf', name='Corbel', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145\n",
      "2023-05-04 21:22:57,675 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\CALISTI.TTF', name='Calisto MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n",
      "2023-05-04 21:22:57,677 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\GILBI___.TTF', name='Gill Sans MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n",
      "2023-05-04 21:22:57,677 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\ROCK.TTF', name='Rockwell', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,678 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\webdings.ttf', name='Webdings', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,680 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\lucon.ttf', name='Lucida Console', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,682 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\MTCORSVA.TTF', name='Monotype Corsiva', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,683 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\PAPYRUS.TTF', name='Papyrus', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,685 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\cour.ttf', name='Courier New', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,687 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\ARIALN.TTF', name='Arial', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 6.613636363636363\n",
      "2023-05-04 21:22:57,688 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\FRAMDCN.TTF', name='Franklin Gothic Medium Cond', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25\n",
      "2023-05-04 21:22:57,689 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\HARLOWSI.TTF', name='Harlow Solid Italic', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n",
      "2023-05-04 21:22:57,690 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\TCM_____.TTF', name='Tw Cen MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,691 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\SCHLBKB.TTF', name='Century Schoolbook', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "2023-05-04 21:22:57,692 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\holomdl2.ttf', name='HoloLens MDL2 Assets', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,693 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\VINERITC.TTF', name='Viner Hand ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,694 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\ROCKBI.TTF', name='Rockwell', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n",
      "2023-05-04 21:22:57,695 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\times.ttf', name='Times New Roman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,697 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\msyi.ttf', name='Microsoft Yi Baiti', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,698 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\phagspab.ttf', name='Microsoft PhagsPa', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "2023-05-04 21:22:57,699 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\cambriaz.ttf', name='Cambria', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n",
      "2023-05-04 21:22:57,699 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\CASTELAR.TTF', name='Castellar', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,701 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\GARAIT.TTF', name='Garamond', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n",
      "2023-05-04 21:22:57,702 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\TEMPSITC.TTF', name='Tempus Sans ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,703 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\FREESCPT.TTF', name='Freestyle Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,704 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\CENTURY.TTF', name='Century', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,704 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\Deng.ttf', name='DengXian', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,705 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\MAIAN.TTF', name='Maiandra GD', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,708 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\COPRGTL.TTF', name='Copperplate Gothic Light', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,709 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\constan.ttf', name='Constantia', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,711 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\LFAXDI.TTF', name='Lucida Fax', style='italic', variant='normal', weight=600, stretch='normal', size='scalable')) = 11.24\n",
      "2023-05-04 21:22:57,714 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\ROCC____.TTF', name='Rockwell Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25\n",
      "2023-05-04 21:22:57,715 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\corbell.ttf', name='Corbel', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145\n",
      "2023-05-04 21:22:57,717 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\Dengl.ttf', name='DengXian', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145\n",
      "2023-05-04 21:22:57,718 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\ENGR.TTF', name='Engravers MT', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n",
      "2023-05-04 21:22:57,719 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\KUNSTLER.TTF', name='Kunstler Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,719 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\courbi.ttf', name='Courier New', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n",
      "2023-05-04 21:22:57,720 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\PARCHM.TTF', name='Parchment', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,721 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\SNAP____.TTF', name='Snap ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,722 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\malgunbd.ttf', name='Malgun Gothic', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "2023-05-04 21:22:57,722 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\CALISTB.TTF', name='Calisto MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "2023-05-04 21:22:57,723 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\VIVALDII.TTF', name='Vivaldi', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n",
      "2023-05-04 21:22:57,724 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\trebucit.ttf', name='Trebuchet MS', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n",
      "2023-05-04 21:22:57,725 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\DUBAI-MEDIUM.TTF', name='Dubai', style='normal', variant='normal', weight=500, stretch='normal', size='scalable')) = 10.145\n",
      "2023-05-04 21:22:57,726 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\BOOKOS.TTF', name='Bookman Old Style', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145\n",
      "2023-05-04 21:22:57,726 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\corbeli.ttf', name='Corbel', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n",
      "2023-05-04 21:22:57,727 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\RAVIE.TTF', name='Ravie', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,728 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\segoeuisl.ttf', name='Segoe UI', style='normal', variant='normal', weight=350, stretch='normal', size='scalable')) = 10.0975\n",
      "2023-05-04 21:22:57,729 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\verdanaz.ttf', name='Verdana', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 4.971363636363637\n",
      "2023-05-04 21:22:57,730 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\GIL_____.TTF', name='Gill Sans MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,731 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\calibrili.ttf', name='Calibri', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145\n",
      "2023-05-04 21:22:57,732 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\ROCCB___.TTF', name='Rockwell Condensed', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535\n",
      "2023-05-04 21:22:57,732 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\corbelz.ttf', name='Corbel', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n",
      "2023-05-04 21:22:57,735 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\consolai.ttf', name='Consolas', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n",
      "2023-05-04 21:22:57,736 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\BOD_CBI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=700, stretch='condensed', size='scalable')) = 11.535\n",
      "2023-05-04 21:22:57,737 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\taile.ttf', name='Microsoft Tai Le', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,739 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\seguisb.ttf', name='Segoe UI', style='normal', variant='normal', weight=600, stretch='normal', size='scalable')) = 10.24\n",
      "2023-05-04 21:22:57,740 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\timesbd.ttf', name='Times New Roman', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "2023-05-04 21:22:57,741 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\BOD_PSTC.TTF', name='Bodoni MT', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145\n",
      "2023-05-04 21:22:57,742 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\COOPBL.TTF', name='Cooper Black', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,743 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\SCHLBKBI.TTF', name='Century Schoolbook', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n",
      "2023-05-04 21:22:57,744 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\LHANDW.TTF', name='Lucida Handwriting', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n",
      "2023-05-04 21:22:57,746 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\ERASLGHT.TTF', name='Eras Light ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,747 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\BELLI.TTF', name='Bell MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n",
      "2023-05-04 21:22:57,748 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\framdit.ttf', name='Franklin Gothic Medium', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n",
      "2023-05-04 21:22:57,749 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\BOOKOSI.TTF', name='Bookman Old Style', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145\n",
      "2023-05-04 21:22:57,750 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\GILC____.TTF', name='Gill Sans MT Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25\n",
      "2023-05-04 21:22:57,750 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\HTOWERT.TTF', name='High Tower Text', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,751 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\Candarab.ttf', name='Candara', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "2023-05-04 21:22:57,752 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\SCRIPTBL.TTF', name='Script MT Bold', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "2023-05-04 21:22:57,752 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\VLADIMIR.TTF', name='Vladimir Script', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,753 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\OLDENGL.TTF', name='Old English Text MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,754 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\MISTRAL.TTF', name='Mistral', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,754 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\BKANT.TTF', name='Book Antiqua', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,755 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\GIGI.TTF', name='Gigi', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,756 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\JOKERMAN.TTF', name='Jokerman', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,757 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\seguibli.ttf', name='Segoe UI', style='italic', variant='normal', weight=900, stretch='normal', size='scalable')) = 11.525\n",
      "2023-05-04 21:22:57,758 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\msyhbd.ttc', name='Microsoft YaHei', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "2023-05-04 21:22:57,759 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\FTLTLT.TTF', name='Footlight MT Light', style='normal', variant='normal', weight=300, stretch='normal', size='scalable')) = 10.145\n",
      "2023-05-04 21:22:57,759 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\calibri.ttf', name='Calibri', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,760 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\TCBI____.TTF', name='Tw Cen MT', style='italic', variant='normal', weight=700, stretch='normal', size='scalable')) = 11.335\n",
      "2023-05-04 21:22:57,762 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\JUICE___.TTF', name='Juice ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,763 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\constani.ttf', name='Constantia', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n",
      "2023-05-04 21:22:57,764 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\BOD_BLAI.TTF', name='Bodoni MT', style='italic', variant='normal', weight=900, stretch='normal', size='scalable')) = 11.525\n",
      "2023-05-04 21:22:57,765 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\CALIFR.TTF', name='Californian FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,766 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\seguiemj.ttf', name='Segoe UI Emoji', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,766 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\TCCB____.TTF', name='Tw Cen MT Condensed', style='normal', variant='normal', weight=700, stretch='condensed', size='scalable')) = 10.535\n",
      "2023-05-04 21:22:57,767 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\IMPRISHA.TTF', name='Imprint MT Shadow', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,768 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\GLSNECB.TTF', name='Gill Sans MT Ext Condensed Bold', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25\n",
      "2023-05-04 21:22:57,768 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\BRLNSR.TTF', name='Berlin Sans FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,769 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\BRADHITC.TTF', name='Bradley Hand ITC', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,770 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\micross.ttf', name='Microsoft Sans Serif', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,771 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\SansSerifCollection.ttf', name='Sans Serif Collection', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,772 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\ALGER.TTF', name='Algerian', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,774 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\PALSCRI.TTF', name='Palace Script MT', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,776 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\TCB_____.TTF', name='Tw Cen MT', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "2023-05-04 21:22:57,778 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\seguili.ttf', name='Segoe UI', style='italic', variant='normal', weight=300, stretch='normal', size='scalable')) = 11.145\n",
      "2023-05-04 21:22:57,780 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\LTYPE.TTF', name='Lucida Sans Typewriter', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,782 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\DUBAI-BOLD.TTF', name='Dubai', style='normal', variant='normal', weight=700, stretch='normal', size='scalable')) = 10.335\n",
      "2023-05-04 21:22:57,783 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\PRISTINA.TTF', name='Pristina', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,785 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\consola.ttf', name='Consolas', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,786 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\ELEPHNT.TTF', name='Elephant', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,787 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\BRUSHSCI.TTF', name='Brush Script MT', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n",
      "2023-05-04 21:22:57,788 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\SIMYOU.TTF', name='YouYuan', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,789 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\LBRITEI.TTF', name='Lucida Bright', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n",
      "2023-05-04 21:22:57,790 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\LSANSI.TTF', name='Lucida Sans', style='italic', variant='normal', weight=400, stretch='normal', size='scalable')) = 11.05\n",
      "2023-05-04 21:22:57,791 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\AGENCYR.TTF', name='Agency FB', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,793 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\GLECB.TTF', name='Gloucester MT Extra Condensed', style='normal', variant='normal', weight=400, stretch='condensed', size='scalable')) = 10.25\n",
      "2023-05-04 21:22:57,793 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\framd.ttf', name='Franklin Gothic Medium', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,795 - matplotlib.font_manager - DEBUG - findfont: score(FontEntry(fname='C:\\\\Windows\\\\Fonts\\\\FRAHV.TTF', name='Franklin Gothic Heavy', style='normal', variant='normal', weight=400, stretch='normal', size='scalable')) = 10.05\n",
      "2023-05-04 21:22:57,796 - matplotlib.font_manager - DEBUG - findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('C:\\\\Users\\\\Qian Lv\\\\AppData\\\\Roaming\\\\Python\\\\Python311\\\\site-packages\\\\matplotlib\\\\mpl-data\\\\fonts\\\\ttf\\\\DejaVuSans.ttf') with score of 0.050000.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-05-04 21:22:57,971 - root - INFO - 完成.\n"
     ]
    }
   ],
   "source": [
    "# 绘波形图和R波位置\n",
    "num_plot_samples = 1024*8\n",
    "logging.info(\"绘制波形图和检测的R波位置 ...\")\n",
    "sig_plot = test_ECG[:num_plot_samples]\n",
    "rpeaks_plot_1 = rpeaks_indices_1[rpeaks_indices_1 <= num_plot_samples]\n",
    "plt.figure()\n",
    "plt.plot(sig_plot, \"g\", label=\"ECG\")\n",
    "plt.grid(True)\n",
    "plt.plot(rpeaks_plot_1, sig_plot[rpeaks_plot_1], \"ro\", label=\"christov_segmenter\")\n",
    "rpeaks_plot_3 = rpeaks_indices_2[rpeaks_indices_2 <= num_plot_samples]\n",
    "plt.plot(rpeaks_plot_3, sig_plot[rpeaks_plot_3], \"b^\", label=\"hamilton_segmenter\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "logging.info(\"完成.\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 计算心率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-05-04 21:28:28,011 - root - INFO - 平均心率: 128.731 / 分钟.\n"
     ]
    }
   ],
   "source": [
    "# API计算心率\n",
    "heart_rate = 60 / (np.mean(np.diff(rpeaks)) / fs)\n",
    "logging.info(\"平均心率: %.3f / 分钟.\" % (heart_rate))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-05-04 21:31:07,085 - root - INFO - 根据R波位置截取心拍, 心拍前窗口：0.362 秒 ~ 心拍后窗口：0.362 秒 ...\n",
      "2023-05-04 21:31:07,086 - root - INFO - 完成. 用时: 0.000000 秒.\n",
      "2023-05-04 21:31:07,087 - root - INFO - 共截取到 28 个心拍, 每个心拍长度为 370 个采样点\n"
     ]
    }
   ],
   "source": [
    "win_before = 0.3625\n",
    "win_after = 0.3625\n",
    "logging.info(\"根据R波位置截取心拍, 心拍前窗口：%.3f 秒 ~ 心拍后窗口：%.3f 秒 ...\" \\\n",
    "             % (win_before, win_after))\n",
    "tic = time.time()\n",
    "beats, rpeaks_beats = ecg.extract_heartbeats(test_ECG, rpeaks_indices_1, fs, win_before, win_after)\n",
    "toc = time.time()\n",
    "logging.info(\"完成. 用时: %f 秒.\" % (toc - tic))\n",
    "logging.info(\"共截取到 %d 个心拍, 每个心拍长度为 %d 个采样点\" % \\\n",
    "             (beats.shape[0], beats.shape[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 将所有心拍标记出来\n",
    "plt.figure()\n",
    "plt.grid(True)\n",
    "for i in range(beats.shape[0]):\n",
    "    plt.plot(beats[i])\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### API自动检测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-05-05 15:47:38,876 - PIL.PngImagePlugin - DEBUG - STREAM b'IHDR' 16 13\n",
      "2023-05-05 15:47:38,877 - PIL.PngImagePlugin - DEBUG - STREAM b'sBIT' 41 4\n",
      "2023-05-05 15:47:38,879 - PIL.PngImagePlugin - DEBUG - b'sBIT' 41 4 (unknown)\n",
      "2023-05-05 15:47:38,880 - PIL.PngImagePlugin - DEBUG - STREAM b'pHYs' 57 9\n",
      "2023-05-05 15:47:38,881 - PIL.PngImagePlugin - DEBUG - STREAM b'IDAT' 78 374\n",
      "2023-05-05 15:47:38,896 - PIL.PngImagePlugin - DEBUG - STREAM b'IHDR' 16 13\n",
      "2023-05-05 15:47:38,900 - PIL.PngImagePlugin - DEBUG - STREAM b'sBIT' 41 4\n",
      "2023-05-05 15:47:38,904 - PIL.PngImagePlugin - DEBUG - b'sBIT' 41 4 (unknown)\n",
      "2023-05-05 15:47:38,909 - PIL.PngImagePlugin - DEBUG - STREAM b'pHYs' 57 9\n",
      "2023-05-05 15:47:38,924 - PIL.PngImagePlugin - DEBUG - STREAM b'IDAT' 78 286\n",
      "2023-05-05 15:47:38,932 - PIL.PngImagePlugin - DEBUG - STREAM b'IHDR' 16 13\n",
      "2023-05-05 15:47:38,934 - PIL.PngImagePlugin - DEBUG - STREAM b'sBIT' 41 4\n",
      "2023-05-05 15:47:38,935 - PIL.PngImagePlugin - DEBUG - b'sBIT' 41 4 (unknown)\n",
      "2023-05-05 15:47:38,938 - PIL.PngImagePlugin - DEBUG - STREAM b'pHYs' 57 9\n",
      "2023-05-05 15:47:38,941 - PIL.PngImagePlugin - DEBUG - STREAM b'IDAT' 78 263\n",
      "2023-05-05 15:47:38,950 - PIL.PngImagePlugin - DEBUG - STREAM b'IHDR' 16 13\n",
      "2023-05-05 15:47:38,955 - PIL.PngImagePlugin - DEBUG - STREAM b'sBIT' 41 4\n",
      "2023-05-05 15:47:38,957 - PIL.PngImagePlugin - DEBUG - b'sBIT' 41 4 (unknown)\n",
      "2023-05-05 15:47:38,959 - PIL.PngImagePlugin - DEBUG - STREAM b'pHYs' 57 9\n",
      "2023-05-05 15:47:38,961 - PIL.PngImagePlugin - DEBUG - STREAM b'IDAT' 78 387\n",
      "2023-05-05 15:47:38,983 - PIL.PngImagePlugin - DEBUG - STREAM b'IHDR' 16 13\n",
      "2023-05-05 15:47:38,987 - PIL.PngImagePlugin - DEBUG - STREAM b'sBIT' 41 4\n",
      "2023-05-05 15:47:38,992 - PIL.PngImagePlugin - DEBUG - b'sBIT' 41 4 (unknown)\n",
      "2023-05-05 15:47:38,995 - PIL.PngImagePlugin - DEBUG - STREAM b'pHYs' 57 9\n",
      "2023-05-05 15:47:38,996 - PIL.PngImagePlugin - DEBUG - STREAM b'IDAT' 78 436\n",
      "2023-05-05 15:47:39,003 - PIL.PngImagePlugin - DEBUG - STREAM b'IHDR' 16 13\n",
      "2023-05-05 15:47:39,004 - PIL.PngImagePlugin - DEBUG - STREAM b'sBIT' 41 4\n",
      "2023-05-05 15:47:39,006 - PIL.PngImagePlugin - DEBUG - b'sBIT' 41 4 (unknown)\n",
      "2023-05-05 15:47:39,006 - PIL.PngImagePlugin - DEBUG - STREAM b'pHYs' 57 9\n",
      "2023-05-05 15:47:39,008 - PIL.PngImagePlugin - DEBUG - STREAM b'IDAT' 78 351\n",
      "2023-05-05 15:47:39,015 - PIL.PngImagePlugin - DEBUG - STREAM b'IHDR' 16 13\n",
      "2023-05-05 15:47:39,016 - PIL.PngImagePlugin - DEBUG - STREAM b'sBIT' 41 4\n",
      "2023-05-05 15:47:39,017 - PIL.PngImagePlugin - DEBUG - b'sBIT' 41 4 (unknown)\n",
      "2023-05-05 15:47:39,018 - PIL.PngImagePlugin - DEBUG - STREAM b'pHYs' 57 9\n",
      "2023-05-05 15:47:39,019 - PIL.PngImagePlugin - DEBUG - STREAM b'IDAT' 78 364\n",
      "2023-05-05 15:47:39,178 - PIL.PngImagePlugin - DEBUG - STREAM b'IHDR' 16 13\n",
      "2023-05-05 15:47:39,181 - PIL.PngImagePlugin - DEBUG - STREAM b'sBIT' 41 4\n",
      "2023-05-05 15:47:39,182 - PIL.PngImagePlugin - DEBUG - b'sBIT' 41 4 (unknown)\n",
      "2023-05-05 15:47:39,183 - PIL.PngImagePlugin - DEBUG - STREAM b'pHYs' 57 9\n",
      "2023-05-05 15:47:39,184 - PIL.PngImagePlugin - DEBUG - STREAM b'IDAT' 78 374\n",
      "2023-05-05 15:47:39,193 - PIL.PngImagePlugin - DEBUG - STREAM b'IHDR' 16 13\n",
      "2023-05-05 15:47:39,194 - PIL.PngImagePlugin - DEBUG - STREAM b'sBIT' 41 4\n",
      "2023-05-05 15:47:39,195 - PIL.PngImagePlugin - DEBUG - b'sBIT' 41 4 (unknown)\n",
      "2023-05-05 15:47:39,196 - PIL.PngImagePlugin - DEBUG - STREAM b'pHYs' 57 9\n",
      "2023-05-05 15:47:39,198 - PIL.PngImagePlugin - DEBUG - STREAM b'IDAT' 78 286\n",
      "2023-05-05 15:47:39,207 - PIL.PngImagePlugin - DEBUG - STREAM b'IHDR' 16 13\n",
      "2023-05-05 15:47:39,208 - PIL.PngImagePlugin - DEBUG - STREAM b'sBIT' 41 4\n",
      "2023-05-05 15:47:39,209 - PIL.PngImagePlugin - DEBUG - b'sBIT' 41 4 (unknown)\n",
      "2023-05-05 15:47:39,210 - PIL.PngImagePlugin - DEBUG - STREAM b'pHYs' 57 9\n",
      "2023-05-05 15:47:39,212 - PIL.PngImagePlugin - DEBUG - STREAM b'IDAT' 78 263\n",
      "2023-05-05 15:47:39,218 - PIL.PngImagePlugin - DEBUG - STREAM b'IHDR' 16 13\n",
      "2023-05-05 15:47:39,220 - PIL.PngImagePlugin - DEBUG - STREAM b'sBIT' 41 4\n",
      "2023-05-05 15:47:39,222 - PIL.PngImagePlugin - DEBUG - b'sBIT' 41 4 (unknown)\n",
      "2023-05-05 15:47:39,223 - PIL.PngImagePlugin - DEBUG - STREAM b'pHYs' 57 9\n",
      "2023-05-05 15:47:39,224 - PIL.PngImagePlugin - DEBUG - STREAM b'IDAT' 78 387\n",
      "2023-05-05 15:47:39,230 - PIL.PngImagePlugin - DEBUG - STREAM b'IHDR' 16 13\n",
      "2023-05-05 15:47:39,231 - PIL.PngImagePlugin - DEBUG - STREAM b'sBIT' 41 4\n",
      "2023-05-05 15:47:39,232 - PIL.PngImagePlugin - DEBUG - b'sBIT' 41 4 (unknown)\n",
      "2023-05-05 15:47:39,233 - PIL.PngImagePlugin - DEBUG - STREAM b'pHYs' 57 9\n",
      "2023-05-05 15:47:39,234 - PIL.PngImagePlugin - DEBUG - STREAM b'IDAT' 78 436\n",
      "2023-05-05 15:47:39,239 - PIL.PngImagePlugin - DEBUG - STREAM b'IHDR' 16 13\n",
      "2023-05-05 15:47:39,241 - PIL.PngImagePlugin - DEBUG - STREAM b'sBIT' 41 4\n",
      "2023-05-05 15:47:39,241 - PIL.PngImagePlugin - DEBUG - b'sBIT' 41 4 (unknown)\n",
      "2023-05-05 15:47:39,242 - PIL.PngImagePlugin - DEBUG - STREAM b'pHYs' 57 9\n",
      "2023-05-05 15:47:39,243 - PIL.PngImagePlugin - DEBUG - STREAM b'IDAT' 78 351\n",
      "2023-05-05 15:47:39,248 - PIL.PngImagePlugin - DEBUG - STREAM b'IHDR' 16 13\n",
      "2023-05-05 15:47:39,250 - PIL.PngImagePlugin - DEBUG - STREAM b'sBIT' 41 4\n",
      "2023-05-05 15:47:39,251 - PIL.PngImagePlugin - DEBUG - b'sBIT' 41 4 (unknown)\n",
      "2023-05-05 15:47:39,252 - PIL.PngImagePlugin - DEBUG - STREAM b'pHYs' 57 9\n",
      "2023-05-05 15:47:39,254 - PIL.PngImagePlugin - DEBUG - STREAM b'IDAT' 78 364\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "ReturnTuple(ts=array([0.00000000e+00, 1.95312500e-03, 3.90625000e-03, ...,\n",
       "       1.59941406e+01, 1.59960938e+01, 1.59980469e+01]), filtered=array([  2.85905545, 259.53147072, 501.37833216, ...,  94.87019988,\n",
       "        89.41556422,  85.99502713]), rpeaks=array([ 152,  387,  632,  962, 1294, 1575, 1818, 2034, 2270, 2490, 2737,\n",
       "       2974, 3201, 3460, 3711, 3975, 4223, 4460, 4697, 4932, 5177, 5414,\n",
       "       5648, 5875, 6087, 6302, 6528, 6754, 6976, 7168, 7385, 7601, 7813]), templates_ts=array([-2.00000000e-01, -1.98039216e-01, -1.96078431e-01, -1.94117647e-01,\n",
       "       -1.92156863e-01, -1.90196078e-01, -1.88235294e-01, -1.86274510e-01,\n",
       "       -1.84313725e-01, -1.82352941e-01, -1.80392157e-01, -1.78431373e-01,\n",
       "       -1.76470588e-01, -1.74509804e-01, -1.72549020e-01, -1.70588235e-01,\n",
       "       -1.68627451e-01, -1.66666667e-01, -1.64705882e-01, -1.62745098e-01,\n",
       "       -1.60784314e-01, -1.58823529e-01, -1.56862745e-01, -1.54901961e-01,\n",
       "       -1.52941176e-01, -1.50980392e-01, -1.49019608e-01, -1.47058824e-01,\n",
       "       -1.45098039e-01, -1.43137255e-01, -1.41176471e-01, -1.39215686e-01,\n",
       "       -1.37254902e-01, -1.35294118e-01, -1.33333333e-01, -1.31372549e-01,\n",
       "       -1.29411765e-01, -1.27450980e-01, -1.25490196e-01, -1.23529412e-01,\n",
       "       -1.21568627e-01, -1.19607843e-01, -1.17647059e-01, -1.15686275e-01,\n",
       "       -1.13725490e-01, -1.11764706e-01, -1.09803922e-01, -1.07843137e-01,\n",
       "       -1.05882353e-01, -1.03921569e-01, -1.01960784e-01, -1.00000000e-01,\n",
       "       -9.80392157e-02, -9.60784314e-02, -9.41176471e-02, -9.21568627e-02,\n",
       "       -9.01960784e-02, -8.82352941e-02, -8.62745098e-02, -8.43137255e-02,\n",
       "       -8.23529412e-02, -8.03921569e-02, -7.84313725e-02, -7.64705882e-02,\n",
       "       -7.45098039e-02, -7.25490196e-02, -7.05882353e-02, -6.86274510e-02,\n",
       "       -6.66666667e-02, -6.47058824e-02, -6.27450980e-02, -6.07843137e-02,\n",
       "       -5.88235294e-02, -5.68627451e-02, -5.49019608e-02, -5.29411765e-02,\n",
       "       -5.09803922e-02, -4.90196078e-02, -4.70588235e-02, -4.50980392e-02,\n",
       "       -4.31372549e-02, -4.11764706e-02, -3.92156863e-02, -3.72549020e-02,\n",
       "       -3.52941176e-02, -3.33333333e-02, -3.13725490e-02, -2.94117647e-02,\n",
       "       -2.74509804e-02, -2.54901961e-02, -2.35294118e-02, -2.15686275e-02,\n",
       "       -1.96078431e-02, -1.76470588e-02, -1.56862745e-02, -1.37254902e-02,\n",
       "       -1.17647059e-02, -9.80392157e-03, -7.84313725e-03, -5.88235294e-03,\n",
       "       -3.92156863e-03, -1.96078431e-03,  2.77555756e-17,  1.96078431e-03,\n",
       "        3.92156863e-03,  5.88235294e-03,  7.84313725e-03,  9.80392157e-03,\n",
       "        1.17647059e-02,  1.37254902e-02,  1.56862745e-02,  1.76470588e-02,\n",
       "        1.96078431e-02,  2.15686275e-02,  2.35294118e-02,  2.54901961e-02,\n",
       "        2.74509804e-02,  2.94117647e-02,  3.13725490e-02,  3.33333333e-02,\n",
       "        3.52941176e-02,  3.72549020e-02,  3.92156863e-02,  4.11764706e-02,\n",
       "        4.31372549e-02,  4.50980392e-02,  4.70588235e-02,  4.90196078e-02,\n",
       "        5.09803922e-02,  5.29411765e-02,  5.49019608e-02,  5.68627451e-02,\n",
       "        5.88235294e-02,  6.07843137e-02,  6.27450980e-02,  6.47058824e-02,\n",
       "        6.66666667e-02,  6.86274510e-02,  7.05882353e-02,  7.25490196e-02,\n",
       "        7.45098039e-02,  7.64705882e-02,  7.84313725e-02,  8.03921569e-02,\n",
       "        8.23529412e-02,  8.43137255e-02,  8.62745098e-02,  8.82352941e-02,\n",
       "        9.01960784e-02,  9.21568627e-02,  9.41176471e-02,  9.60784314e-02,\n",
       "        9.80392157e-02,  1.00000000e-01,  1.01960784e-01,  1.03921569e-01,\n",
       "        1.05882353e-01,  1.07843137e-01,  1.09803922e-01,  1.11764706e-01,\n",
       "        1.13725490e-01,  1.15686275e-01,  1.17647059e-01,  1.19607843e-01,\n",
       "        1.21568627e-01,  1.23529412e-01,  1.25490196e-01,  1.27450980e-01,\n",
       "        1.29411765e-01,  1.31372549e-01,  1.33333333e-01,  1.35294118e-01,\n",
       "        1.37254902e-01,  1.39215686e-01,  1.41176471e-01,  1.43137255e-01,\n",
       "        1.45098039e-01,  1.47058824e-01,  1.49019608e-01,  1.50980392e-01,\n",
       "        1.52941176e-01,  1.54901961e-01,  1.56862745e-01,  1.58823529e-01,\n",
       "        1.60784314e-01,  1.62745098e-01,  1.64705882e-01,  1.66666667e-01,\n",
       "        1.68627451e-01,  1.70588235e-01,  1.72549020e-01,  1.74509804e-01,\n",
       "        1.76470588e-01,  1.78431373e-01,  1.80392157e-01,  1.82352941e-01,\n",
       "        1.84313725e-01,  1.86274510e-01,  1.88235294e-01,  1.90196078e-01,\n",
       "        1.92156863e-01,  1.94117647e-01,  1.96078431e-01,  1.98039216e-01,\n",
       "        2.00000000e-01,  2.01960784e-01,  2.03921569e-01,  2.05882353e-01,\n",
       "        2.07843137e-01,  2.09803922e-01,  2.11764706e-01,  2.13725490e-01,\n",
       "        2.15686275e-01,  2.17647059e-01,  2.19607843e-01,  2.21568627e-01,\n",
       "        2.23529412e-01,  2.25490196e-01,  2.27450980e-01,  2.29411765e-01,\n",
       "        2.31372549e-01,  2.33333333e-01,  2.35294118e-01,  2.37254902e-01,\n",
       "        2.39215686e-01,  2.41176471e-01,  2.43137255e-01,  2.45098039e-01,\n",
       "        2.47058824e-01,  2.49019608e-01,  2.50980392e-01,  2.52941176e-01,\n",
       "        2.54901961e-01,  2.56862745e-01,  2.58823529e-01,  2.60784314e-01,\n",
       "        2.62745098e-01,  2.64705882e-01,  2.66666667e-01,  2.68627451e-01,\n",
       "        2.70588235e-01,  2.72549020e-01,  2.74509804e-01,  2.76470588e-01,\n",
       "        2.78431373e-01,  2.80392157e-01,  2.82352941e-01,  2.84313725e-01,\n",
       "        2.86274510e-01,  2.88235294e-01,  2.90196078e-01,  2.92156863e-01,\n",
       "        2.94117647e-01,  2.96078431e-01,  2.98039216e-01,  3.00000000e-01,\n",
       "        3.01960784e-01,  3.03921569e-01,  3.05882353e-01,  3.07843137e-01,\n",
       "        3.09803922e-01,  3.11764706e-01,  3.13725490e-01,  3.15686275e-01,\n",
       "        3.17647059e-01,  3.19607843e-01,  3.21568627e-01,  3.23529412e-01,\n",
       "        3.25490196e-01,  3.27450980e-01,  3.29411765e-01,  3.31372549e-01,\n",
       "        3.33333333e-01,  3.35294118e-01,  3.37254902e-01,  3.39215686e-01,\n",
       "        3.41176471e-01,  3.43137255e-01,  3.45098039e-01,  3.47058824e-01,\n",
       "        3.49019608e-01,  3.50980392e-01,  3.52941176e-01,  3.54901961e-01,\n",
       "        3.56862745e-01,  3.58823529e-01,  3.60784314e-01,  3.62745098e-01,\n",
       "        3.64705882e-01,  3.66666667e-01,  3.68627451e-01,  3.70588235e-01,\n",
       "        3.72549020e-01,  3.74509804e-01,  3.76470588e-01,  3.78431373e-01,\n",
       "        3.80392157e-01,  3.82352941e-01,  3.84313725e-01,  3.86274510e-01,\n",
       "        3.88235294e-01,  3.90196078e-01,  3.92156863e-01,  3.94117647e-01,\n",
       "        3.96078431e-01,  3.98039216e-01]), templates=array([[  79.01706885,   92.02171847,  103.23739997, ...,  -27.58700037,\n",
       "         -41.17008169,  -47.86423888],\n",
       "       [  29.50931554,   39.22740849,   49.95219457, ...,  112.95196975,\n",
       "          88.06162723,   63.96295486],\n",
       "       [ 170.24623251,  153.53128112,  141.08389812, ...,  -23.68490211,\n",
       "         -28.38143455,  -32.11551002],\n",
       "       ...,\n",
       "       [ 141.20829493,  124.55001134,  107.91892439, ...,    5.58929445,\n",
       "         -67.27249001, -161.29233965],\n",
       "       [  48.50486438,   39.66560334,   33.48565408, ..., -330.5968865 ,\n",
       "        -418.10200114, -456.18342578],\n",
       "       [  86.1169011 ,   75.02335824,   66.13512656, ..., -150.44596789,\n",
       "        -246.05273855, -342.68055848]]), heart_rate_ts=array([ 0.75585938,  1.234375  ,  1.87890625,  2.52734375,  3.07617188,\n",
       "        3.55078125,  3.97265625,  4.43359375,  4.86328125,  5.34570312,\n",
       "        5.80859375,  6.25195312,  6.7578125 ,  7.24804688,  7.76367188,\n",
       "        8.24804688,  8.7109375 ,  9.17382812,  9.6328125 , 10.11132812,\n",
       "       10.57421875, 11.03125   , 11.47460938, 11.88867188, 12.30859375,\n",
       "       12.75      , 13.19140625, 13.625     , 14.        , 14.42382812,\n",
       "       14.84570312, 15.25976562]), heart_rate=array([128.94485454, 116.40068948, 103.66959489,  98.31495766,\n",
       "       109.42457233, 125.98860624, 132.93715561, 137.34269246,\n",
       "       131.39277493, 131.20969548, 129.77437309, 127.85356275,\n",
       "       125.44362444, 119.12137107, 120.87501412, 123.28495242,\n",
       "       127.70382469, 129.98797019, 128.57713751, 128.57713751,\n",
       "       128.76335318, 132.07756697, 137.17270271, 141.03992593,\n",
       "       141.23952828, 138.247376  , 136.74559515, 144.76919397,\n",
       "       146.64839955, 147.92968083, 142.89823429, 144.01118099]))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ecg.ecg(signal=test_ECG, sampling_rate=512, show=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 对硬件过来的信号进行二分类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "length = len(test_ECG)\n",
    "ECG_Signal = np.empty(shape=[0,370])\n",
    "for site in rpeaks_indices_2:\n",
    "    if 185< site < length-185:\n",
    "        ECG_signal = test_ECG.flatten().tolist()[site-185:site+185]\n",
    "        ECG_Signal = np.row_stack((ECG_Signal,ECG_signal))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.io as scio\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "# 标准化\n",
    "def standscaler_demo(date):\n",
    "    transfer = StandardScaler()\n",
    "    date1 = transfer.fit_transform(date)\n",
    "    return date1\n",
    "\n",
    "# 数据读进来处理的封装函数，只需要进行滤波操作和标准化操作\n",
    "def ECG_process(ECG):\n",
    "    ECG_filtered = np.zeros(ECG.shape)\n",
    "    for line in range(0,ECG.shape[0]):\n",
    "        ECG_filtered[line] = Wavelet_transform(ECG[line])\n",
    "    ECG_standed = standscaler_demo(ECG_filtered)\n",
    "    return ECG_filtered,ECG_standed   # 返回值为两个数组\n",
    "\n",
    "ECG_filtered,ECG_standed = ECG_process(ECG_Signal) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "预测值是：[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]\n"
     ]
    }
   ],
   "source": [
    "# 模型的加载和使用\n",
    "with open(\"II. Classification(SVC)2.pickle\",\"rb\") as f:\n",
    "    SVM_load = pickle.load(f)\n",
    "\n",
    "y_pre_svc = SVM_load.predict(ECG_filtered)\n",
    "print(\"预测值是：{}\".format(y_pre_svc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_num = int(input(\"请输入你想要绘制的ecg_filtered序号:\"))\n",
    "plt.figure(figsize = (10, 4))\n",
    "plt.subplot(1,2,1)\n",
    "plt.title(\"ECG_filtered\",color=\"g\")\n",
    "plt.plot(ECG_filtered[draw_num])\n",
    "plt.subplot(1,2,2)\n",
    "plt.title(\"ECG_unfiltered\",color=\"r\")\n",
    "plt.plot(ECG_Signal[draw_num])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### 这里可以将异常数据写入数据库"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 经过筛选的数据进入由MIT数据集训练的五分类模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 删掉预测为零的提取心电\n",
    "\n",
    "for i in range(0,y_pre_svc.shape[0]):\n",
    "    if y_pre_svc[i] == 0:\n",
    "        ECG_filtered = np.delete(ECG_filtered[i],i,axis=0)\n",
    "        # print(predict_array[i])\n",
    "# signal"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 下采样\n",
    "##### 将512HZ数据重新采样为360HZ,以便模型更好的提取本地特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 进行下采样370个点到260\n",
    "from scipy import signal\n",
    "\n",
    "def resample_ECG(ECG_filtered):\n",
    "    # data1 = data / 4096 * 1.6 - 0.6\n",
    "    resample_array = np.ones([ECG_filtered.shape[0],260])\n",
    "    count = 0\n",
    "    for i in ECG_filtered:\n",
    "        # resample_list.append(scipy.signal.resample(i, 717, t=None, axis=0, window=None))\n",
    "        resample_array[count] = signal.resample(i, 260, t=None, axis=0, window=None)\n",
    "        count += 1\n",
    "    return resample_array\n",
    "resample_array = resample_ECG(ECG_filtered)\n",
    "plt.plot(resample_ECG(ECG_filtered)[0])\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 加载模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 141ms/step\n"
     ]
    }
   ],
   "source": [
    "# 0.9931\n",
    "# tensorflow有毒,加载模型不能存放在中文路径下面\n",
    "import tensorflow as tf\n",
    "\n",
    "model = tf.keras.models.load_model('D:/VSproject/model/Five_classifications.h5')\n",
    "model1 = model.predict(resample_array)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 五分类lableSet = ['F','N','Q','S','V']分别对应[0,1,2,3,4]\n",
    "\n",
    "AAMI规定心电节拍可以分为五大类：\n",
    "- N(正常或者束支传导阻滞节拍)\n",
    "- s(室上性异常节拍)\n",
    "- V(心室异常节拍)\n",
    "- F(融合节拍)\n",
    "- Q(未能分类的节拍)\n",
    "以及无法识读的u类、辅助无法识读标志的x类和0类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "五分类预测的softmax输出:\n",
      "[[8.40952463e-09 9.94997859e-01 6.92920679e-18 1.88705130e-07\n",
      "  5.00186067e-03]\n",
      " [3.93304445e-10 9.98223722e-01 7.96623268e-20 3.84492331e-08\n",
      "  1.77624437e-03]\n",
      " [4.25868230e-09 9.98067081e-01 1.92323484e-18 4.83940255e-07\n",
      "  1.93246512e-03]\n",
      " [1.42907970e-06 9.64953721e-01 3.01221423e-13 5.37072219e-06\n",
      "  3.50395143e-02]\n",
      " [3.33905015e-10 9.99890447e-01 6.76872255e-23 2.05179718e-09\n",
      "  1.09493747e-04]\n",
      " [1.47212251e-08 9.95892525e-01 1.08164048e-17 1.89085668e-07\n",
      "  4.10721032e-03]\n",
      " [2.87481838e-09 9.99117315e-01 3.98370984e-19 9.72438841e-08\n",
      "  8.82551132e-04]\n",
      " [2.97704594e-09 9.95430350e-01 2.19540228e-18 9.29956983e-08\n",
      "  4.56955004e-03]\n",
      " [2.30602981e-09 9.95872200e-01 1.97286422e-18 3.58634118e-08\n",
      "  4.12782654e-03]\n",
      " [1.17852226e-08 9.95670915e-01 1.64850379e-17 2.57356618e-07\n",
      "  4.32884460e-03]\n",
      " [3.00950092e-08 9.95541275e-01 3.83118858e-17 2.61857878e-07\n",
      "  4.45843488e-03]\n",
      " [2.31043273e-08 9.97031212e-01 4.94051984e-17 4.32705519e-07\n",
      "  2.96824682e-03]\n",
      " [3.53081973e-08 9.96101737e-01 1.23379373e-17 3.25005374e-07\n",
      "  3.89787112e-03]\n",
      " [4.49588242e-08 9.92384374e-01 3.87736293e-16 5.17678075e-07\n",
      "  7.61516858e-03]\n",
      " [2.27028174e-08 9.97036457e-01 2.05308690e-16 9.22412084e-07\n",
      "  2.96266004e-03]\n",
      " [4.27535731e-08 9.92371678e-01 1.82671959e-16 5.91293485e-07\n",
      "  7.62773957e-03]\n",
      " [2.94490530e-08 9.94387925e-01 4.87542688e-16 8.72561202e-07\n",
      "  5.61126880e-03]\n",
      " [1.82728694e-08 9.95464861e-01 1.20157885e-17 2.17609411e-07\n",
      "  4.53493558e-03]\n",
      " [3.24061453e-08 9.94549453e-01 5.62048249e-17 3.11993745e-07\n",
      "  5.45012811e-03]\n",
      " [9.23872800e-09 9.97102201e-01 8.11209823e-18 2.40111234e-07\n",
      "  2.89754593e-03]\n",
      " [5.76377168e-08 9.91659462e-01 3.96359936e-16 6.80155608e-07\n",
      "  8.33980180e-03]\n",
      " [7.81958249e-08 9.91754651e-01 5.91706936e-16 8.97817074e-07\n",
      "  8.24431982e-03]\n",
      " [3.58886716e-08 9.92288888e-01 1.20339268e-16 2.90006398e-07\n",
      "  7.71089224e-03]\n",
      " [6.65092514e-08 9.91374075e-01 3.83311128e-16 5.14920202e-07\n",
      "  8.62537697e-03]\n",
      " [1.77578592e-08 9.95567381e-01 1.10461352e-17 2.12416310e-07\n",
      "  4.43238998e-03]\n",
      " [1.87373903e-08 9.94585216e-01 1.48174041e-16 5.10776147e-07\n",
      "  5.41432900e-03]\n",
      " [5.91196603e-08 9.91435826e-01 4.26152652e-16 6.88891078e-07\n",
      "  8.56338255e-03]\n",
      " [2.25975498e-07 9.88377988e-01 1.26020164e-15 1.63653817e-06\n",
      "  1.16201388e-02]\n",
      " [1.21455841e-07 9.91906166e-01 4.03609421e-15 1.10829899e-06\n",
      "  8.09265207e-03]\n",
      " [6.04587740e-08 9.90863264e-01 4.41872048e-16 7.87756505e-07\n",
      "  9.13572311e-03]\n",
      " [7.60547607e-08 9.94744897e-01 6.99831327e-17 3.54780525e-07\n",
      "  5.25469519e-03]\n",
      " [3.96176993e-08 9.93124187e-01 2.14137605e-16 5.17604690e-07\n",
      "  6.87537529e-03]]\n",
      "shape为:(32, 5)\n",
      "----------------------------------\n",
      "onehot编码输出为:\n",
      "[[0. 1. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0.]]\n",
      "onehot输出的维度为:(32, 5)\n",
      "----------------------------------\n",
      "最终分类为:[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n"
     ]
    }
   ],
   "source": [
    "from keras.utils import to_categorical\n",
    "\n",
    "print('五分类预测的softmax输出:\\n{}'.format(model1))\n",
    "print(\"shape为:{}\".format(model1.shape))\n",
    "print('----------------------------------')\n",
    "# 将其转化为onehot编码\n",
    "one_hot_preds = to_categorical(model1.argmax(axis=1), num_classes=5)\n",
    "print(\"onehot编码输出为:\\n{}\".format(one_hot_preds))\n",
    "print(\"onehot输出的维度为:{}\".format(one_hot_preds.shape))\n",
    "print('----------------------------------')\n",
    "# 再将onehot编码输出转化为直观的一维数组\n",
    "data_pre = [np.argmax(one_hot)for one_hot in one_hot_preds]\n",
    "print(\"最终分类为:{}\".format(data_pre))\n",
    "\n",
    "\n"
   ]
  }
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